Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

3.
Overview: DQ Definition <ul><ul><li>Data are of high quality &quot;if they are fit for their intended uses in operations , decision making and planning &quot; (J.M. Juran). </li></ul></ul><ul><ul><li>The state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use. </li></ul></ul><ul><li>DQ Impact : Organizations with poor data quality spend time working with conflicting reports and flawed business plans, resulting in erroneous decisions that are made with outdated, inconsistent, and invalid data </li></ul>DQ Management Overview DQ Testing Case Study Close

4.
Overview: DQ Stats <ul><ul><li>“ End users spend as much as 40-50% of a typical IT budget reworking data in one application to make it work with another”. The high cost of low data quality. </li></ul></ul><ul><ul><li>The Data Warehouse Institute estimates that bad customer data costs American companies upwards of $600billion dollars per year By Wayne W. Eckerson </li></ul></ul><ul><li>POOR Data Quality can kill your business!!!! </li></ul>DQ Management Overview DQ Testing Case Study Close

9.
Rule #2: Completeness All the data under consideration at the Source and Target should be same at a given point of time satisfying the business rules. DQ Management Source Table Target Table Overview DQ Testing Case Study Close

11.
Rule #3: Consistency This ensures that each user observes a consistent view of the data, including changes made by transactions There is data inconsistency between the Source & Target if the same data is stored in different formats or contain different values at different places. DQ Management Overview DQ Testing Case Study Close

20.
Rule #6: RI If there are child records for which no corresponding parent records existing then they are called “Orphan Records” Logical relationship rules between parent & child tables should be defined by business. DQ Management Overview DQ Testing Case Study Close

27.
Rule #10: Timeliness <ul><li>Defines if data required is available when required as per SLA </li></ul><ul><li>Example #1: Data Freshness </li></ul><ul><ul><li>If everyday data is pulled 24 times and target doesn’t get even for one cycle, “data freshness” get impacted and users see old data which can impact business decisions. </li></ul></ul><ul><li>For decision making & mission critical system, timely availability of information is must. </li></ul>DQ Management Overview DQ Testing Case Study Close

36.
DQ Jargons <ul><li>DATA GOVERNANCE </li></ul><ul><ul><li>Data governance (DG) refers to the overall management of the availability, usability, integrity, and security of the data employed in an enterprise </li></ul></ul><ul><ul><li>Data governance program includes a governing body or council, a defined set of procedures, and a plan to execute those procedures </li></ul></ul><ul><li>DATA STEWARDS </li></ul><ul><ul><li>Data Stewards are those individuals ultimately responsible for the definition, management, control, integrity or maintenance of Enterprise data. </li></ul></ul><ul><li>DATA INTEGRITY </li></ul><ul><ul><li>Data integrity is the assurance that data is correct and consistent--that the data correctly reflects the &quot;real&quot; world. </li></ul></ul>DQ Management Overview DQ Testing Case Study Close